GAMA-12 master catalogue

Checks and diagnostics

In [1]:
from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))
This notebook was run with herschelhelp_internal version: 
6b6e8f7 (Thu Oct 5 17:35:30 2017 +0100)
In [2]:
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
plt.style.use('ggplot')

import locale
locale.setlocale(locale.LC_ALL, 'en_GB')

import os
import time
import itertools

from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import units as u
from astropy import visualization as vis
import numpy as np
from matplotlib_venn import venn3, venn2

from herschelhelp_internal.masterlist import nb_compare_mags, nb_ccplots, nb_histograms
In [3]:
OUT_DIR = os.environ.get('OUT_DIR', "./data")
#SUFFIX = os.environ.get('SUFFIX', time.strftime("_%Y%m%d"))
SUFFIX = "_20171018"

master_catalogue = Table.read("{}/master_catalogue_gama-12{}.fits".format(OUT_DIR, SUFFIX))

I - Summary of wavelength domains

In [4]:
flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']
In [5]:
venn2(
    [
        np.sum(flag_obs == 1),
        np.sum(flag_obs == 2),
        np.sum(flag_obs == 3)
    ],
    set_labels=('Optical', 'near-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");
In [6]:
venn2(
    [
        np.sum(flag_det[flag_obs == 3] == 1),
        np.sum(flag_det[flag_obs == 3] == 2),
        np.sum(flag_det[flag_obs == 3] == 3)
    ],
    set_labels=('near-IR', 'Optical'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/np.sum(flag_det != 0)))
)
plt.title("Detection of the {} sources detected\n in any wavelength domains "
          "(among {} sources)".format(
              locale.format('%d', np.sum(flag_det != 0), grouping=True),
              locale.format('%d', len(flag_det), grouping=True)));

II - Comparing magnitudes in similar filters

The master list if composed of several catalogues containing magnitudes in similar filters on different instruments. We are comparing the magnitudes in these corresponding filters.

In [7]:
u_bands = [           "KIDS u"]
g_bands = ["DECam g", "KIDS g", "SUPRIME g", "GPC1 g"]
r_bands = ["DECam r", "KIDS r", "SUPRIME r", "GPC1 r"]
i_bands = ["DECam i", "KIDS i", "SUPRIME i", "GPC1 i"]
z_bands = ["DECam z",           "SUPRIME z", "GPC1 z"]
y_bands = ["DECam y",           "SUPRIME y", "GPC1 y"]
In [8]:
for band in g_bands:
    print(band, 'max:', np.nanmax(master_catalogue["m_{}".format(band.replace(" ", "_").lower())]))
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:355: RuntimeWarning: All-NaN axis encountered
  warnings.warn("All-NaN axis encountered", RuntimeWarning)
DECam g max: nan
KIDS g max: 43.5271
SUPRIME g max: nan
GPC1 g max: 28.9741001129

The Suprime g magnitudes are very wrong. They are almost all between 0 and 1.

II.a - Comparing depths

We compare the histograms of the total aperture magnitudes of similar bands.

In [9]:
for bands in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
    nb_histograms(master_catalogue, colnames, bands)
HELP warning: the column m_decam_g (DECam g) is empty.
HELP warning: the column m_suprime_g (SUPRIME g) is empty.
HELP warning: the column m_decam_z (DECam z) is empty.

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

In [10]:
for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    for band1, band2 in itertools.combinations(band_of_a_kind, 2):
        
        basecol1, basecol2 = band1.replace(" ", "_").lower(), band2.replace(" ", "_").lower()
        
        col1, col2 = "m_ap_{}".format(basecol1), "m_ap_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (aperture)".format(band1), "{} (aperture)".format(band2)))
        
        col1, col2 = "m_{}".format(basecol1), "m_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (total)".format(band1), "{} (total)".format(band2)))
No sources have both DECam g (aperture) and KIDS g (aperture) values.
No sources have both DECam g (total) and KIDS g (total) values.
No sources have both DECam g (aperture) and SUPRIME g (aperture) values.
No sources have both DECam g (total) and SUPRIME g (total) values.
No sources have both DECam g (aperture) and GPC1 g (aperture) values.
No sources have both DECam g (total) and GPC1 g (total) values.
SUPRIME g (aperture) - KIDS g (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.17
- 1% percentile: -1.7694091796874998
- 99% percentile: 1.1416382789611785
No sources have both KIDS g (total) and SUPRIME g (total) values.
GPC1 g (aperture) - KIDS g (aperture):
- Median: -0.33
- Median Absolute Deviation: 0.33
- 1% percentile: -2.075102996826172
- 99% percentile: 2.269778823852544
GPC1 g (total) - KIDS g (total):
- Median: -0.09
- Median Absolute Deviation: 0.16
- 1% percentile: -1.5102294158935545
- 99% percentile: 0.7436729240417477
GPC1 g (aperture) - SUPRIME g (aperture):
- Median: -0.25
- Median Absolute Deviation: 0.35
- 1% percentile: -2.14129150390625
- 99% percentile: 2.4541178894042948
No sources have both SUPRIME g (total) and GPC1 g (total) values.
KIDS r (aperture) - DECam r (aperture):
- Median: -0.57
- Median Absolute Deviation: 0.42
- 1% percentile: -3.5359573364257812
- 99% percentile: 0.734039306640625
KIDS r (total) - DECam r (total):
- Median: -0.69
- Median Absolute Deviation: 0.43
- 1% percentile: -3.254582099914551
- 99% percentile: 0.992661209106446
SUPRIME r (aperture) - DECam r (aperture):
- Median: -0.61
- Median Absolute Deviation: 0.41
- 1% percentile: -3.591978073120117
- 99% percentile: 0.9319587707519483
SUPRIME r (total) - DECam r (total):
- Median: -0.84
- Median Absolute Deviation: 0.41
- 1% percentile: -3.550533676147461
- 99% percentile: 1.0033042907714531
GPC1 r (aperture) - DECam r (aperture):
- Median: -1.00
- Median Absolute Deviation: 0.37
- 1% percentile: -3.2967313289642335
- 99% percentile: 0.7026295661926285
GPC1 r (total) - DECam r (total):
- Median: -0.86
- Median Absolute Deviation: 0.37
- 1% percentile: -2.5506317138671877
- 99% percentile: 0.4100577354431221
SUPRIME r (aperture) - KIDS r (aperture):
- Median: -0.04
- Median Absolute Deviation: 0.11
- 1% percentile: -0.8898246765136719
- 99% percentile: 0.9822139739990225
SUPRIME r (total) - KIDS r (total):
- Median: -0.21
- Median Absolute Deviation: 0.19
- 1% percentile: -1.4218847274780273
- 99% percentile: 1.2361030578613281
GPC1 r (aperture) - KIDS r (aperture):
- Median: -0.18
- Median Absolute Deviation: 0.23
- 1% percentile: -1.1846766662597656
- 99% percentile: 1.5284150695800705
GPC1 r (total) - KIDS r (total):
- Median: 0.04
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8568764495849609
- 99% percentile: 0.7611067199706989
GPC1 r (aperture) - SUPRIME r (aperture):
- Median: -0.15
- Median Absolute Deviation: 0.24
- 1% percentile: -1.3660277557373046
- 99% percentile: 1.5567139434814363
GPC1 r (total) - SUPRIME r (total):
- Median: 0.14
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8839898109436035
- 99% percentile: 0.8461173057556155
KIDS i (aperture) - DECam i (aperture):
- Median: -0.25
- Median Absolute Deviation: 0.33
- 1% percentile: -2.8722589111328123
- 99% percentile: 1.304483337402342
KIDS i (total) - DECam i (total):
- Median: -0.26
- Median Absolute Deviation: 0.33
- 1% percentile: -1.8559133911132812
- 99% percentile: 2.186717739105226
SUPRIME i (aperture) - DECam i (aperture):
- Median: -0.37
- Median Absolute Deviation: 0.34
- 1% percentile: -3.2666903495788575
- 99% percentile: 0.931794548034671
SUPRIME i (total) - DECam i (total):
- Median: -0.54
- Median Absolute Deviation: 0.31
- 1% percentile: -2.726251163482666
- 99% percentile: 1.2671364402771115
GPC1 i (aperture) - DECam i (aperture):
- Median: -0.46
- Median Absolute Deviation: 0.25
- 1% percentile: -2.445783233642578
- 99% percentile: 0.6057321166992189
GPC1 i (total) - DECam i (total):
- Median: -0.29
- Median Absolute Deviation: 0.25
- 1% percentile: -1.6301992416381834
- 99% percentile: 0.7034828186035158
SUPRIME i (aperture) - KIDS i (aperture):
- Median: -0.08
- Median Absolute Deviation: 0.21
- 1% percentile: -2.0993301010131837
- 99% percentile: 1.3187004470825237
SUPRIME i (total) - KIDS i (total):
- Median: -0.30
- Median Absolute Deviation: 0.30
- 1% percentile: -3.1144363403320314
- 99% percentile: 1.3730266189575193
GPC1 i (aperture) - KIDS i (aperture):
- Median: -0.17
- Median Absolute Deviation: 0.20
- 1% percentile: -1.0249346923828124
- 99% percentile: 0.8876734161376945
GPC1 i (total) - KIDS i (total):
- Median: 0.03
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8408512878417969
- 99% percentile: 0.7019467926025378
GPC1 i (aperture) - SUPRIME i (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1620124816894533
- 99% percentile: 0.9892982482910151
GPC1 i (total) - SUPRIME i (total):
- Median: 0.19
- Median Absolute Deviation: 0.11
- 1% percentile: -0.7631856536865234
- 99% percentile: 0.8027851486206053
No sources have both DECam z (aperture) and SUPRIME z (aperture) values.
No sources have both DECam z (total) and SUPRIME z (total) values.
No sources have both DECam z (aperture) and GPC1 z (aperture) values.
No sources have both DECam z (total) and GPC1 z (total) values.
GPC1 z (aperture) - SUPRIME z (aperture):
- Median: -0.08
- Median Absolute Deviation: 0.20
- 1% percentile: -1.0366680145263671
- 99% percentile: 1.1790359497070313
GPC1 z (total) - SUPRIME z (total):
- Median: 0.18
- Median Absolute Deviation: 0.12
- 1% percentile: -0.6331187438964844
- 99% percentile: 0.8256746673583981
SUPRIME y (aperture) - DECam y (aperture):
- Median: -0.05
- Median Absolute Deviation: 0.20
- 1% percentile: -1.866614532470703
- 99% percentile: 1.233930130004885
SUPRIME y (total) - DECam y (total):
- Median: -0.10
- Median Absolute Deviation: 0.16
- 1% percentile: -1.1705833435058595
- 99% percentile: 1.3771499633789048
GPC1 y (aperture) - DECam y (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.18
- 1% percentile: -1.5973902893066405
- 99% percentile: 2.21849609375
GPC1 y (total) - DECam y (total):
- Median: 0.13
- Median Absolute Deviation: 0.15
- 1% percentile: -0.8611082077026367
- 99% percentile: 1.1647806167602512
GPC1 y (aperture) - SUPRIME y (aperture):
- Median: -0.14
- Median Absolute Deviation: 0.28
- 1% percentile: -1.371766586303711
- 99% percentile: 2.32575355529785
GPC1 y (total) - SUPRIME y (total):
- Median: 0.17
- Median Absolute Deviation: 0.16
- 1% percentile: -0.8742595672607422
- 99% percentile: 0.9855241775512698

III - Comparing magnitudes to reference bands

Cross-match the master list to SDSS and 2MASS to compare its magnitudes to SDSS and 2MASS ones.

In [11]:
master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.a - Comparing u, g, r, i, and z bands to SDSS

The catalogue is cross-matched to SDSS-DR13 withing 0.2 arcsecond.

We compare the u, g, r, i, and z magnitudes to those from SDSS using fiberMag for the aperture magnitude and petroMag for the total magnitude.

In [12]:
sdss = Table.read("../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_GAMA-12.fits")
sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)

idx, d2d, _ = sdss_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

sdss = sdss[mask]
ml_sdss_idx = idx[mask]
In [13]:
for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands]:
    for band in band_of_a_kind:
        
        sdss_mag_ap = sdss["fiberMag_{}".format(band[-1])]
        master_cat_mag_ap = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
    
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (fiberMag)".format(band[-1]), "{} (aperture)".format(band)))
    
        sdss_mag_tot = sdss["petroMag_{}".format(band[-1])]
        master_cat_mag_tot = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
        
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (petroMag)".format(band[-1]), "{} (total)".format(band)))
KIDS u (aperture) - SDSS u (fiberMag):
- Median: 0.10
- Median Absolute Deviation: 0.42
- 1% percentile: -1.6068734741210937
- 99% percentile: 2.954749107360839
KIDS u (total) - SDSS u (petroMag):
- Median: 0.10
- Median Absolute Deviation: 0.42
- 1% percentile: -1.6068734741210937
- 99% percentile: 2.954749107360839
No sources have both SDSS g (fiberMag) and DECam g (aperture) values.
No sources have both SDSS g (petroMag) and DECam g (total) values.
KIDS g (aperture) - SDSS g (fiberMag):
- Median: -0.17
- Median Absolute Deviation: 0.16
- 1% percentile: -0.9890386962890625
- 99% percentile: 1.0339902496337894
KIDS g (total) - SDSS g (petroMag):
- Median: -0.17
- Median Absolute Deviation: 0.16
- 1% percentile: -0.9890386962890625
- 99% percentile: 1.0339902496337894
SUPRIME g (aperture) - SDSS g (fiberMag):
- Median: -0.27
- Median Absolute Deviation: 0.17
- 1% percentile: -1.1210815048217775
- 99% percentile: 1.0015515518188485
SUPRIME g (total) - SDSS g (petroMag):
- Median: -0.27
- Median Absolute Deviation: 0.17
- 1% percentile: -1.1210815048217775
- 99% percentile: 1.0015515518188485
GPC1 g (aperture) - SDSS g (fiberMag):
- Median: -0.52
- Median Absolute Deviation: 0.26
- 1% percentile: -1.8783206939697266
- 99% percentile: 1.920540351867659
GPC1 g (total) - SDSS g (petroMag):
- Median: -0.52
- Median Absolute Deviation: 0.26
- 1% percentile: -1.8783206939697266
- 99% percentile: 1.920540351867659
DECam r (aperture) - SDSS r (fiberMag):
- Median: 0.52
- Median Absolute Deviation: 0.36
- 1% percentile: -0.49354766845703124
- 99% percentile: 2.732038574218751
DECam r (total) - SDSS r (petroMag):
- Median: 0.52
- Median Absolute Deviation: 0.36
- 1% percentile: -0.49354766845703124
- 99% percentile: 2.732038574218751
KIDS r (aperture) - SDSS r (fiberMag):
- Median: -0.23
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8954749298095703
- 99% percentile: 0.4217640686035158
KIDS r (total) - SDSS r (petroMag):
- Median: -0.23
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8954749298095703
- 99% percentile: 0.4217640686035158
SUPRIME r (aperture) - SDSS r (fiberMag):
- Median: -0.26
- Median Absolute Deviation: 0.12
- 1% percentile: -0.951361083984375
- 99% percentile: 0.6180745697021484
SUPRIME r (total) - SDSS r (petroMag):
- Median: -0.26
- Median Absolute Deviation: 0.12
- 1% percentile: -0.951361083984375
- 99% percentile: 0.6180745697021484
GPC1 r (aperture) - SDSS r (fiberMag):
- Median: -0.43
- Median Absolute Deviation: 0.16
- 1% percentile: -1.291998748779297
- 99% percentile: 0.9754924011230486
GPC1 r (total) - SDSS r (petroMag):
- Median: -0.43
- Median Absolute Deviation: 0.16
- 1% percentile: -1.291998748779297
- 99% percentile: 0.9754924011230486
DECam i (aperture) - SDSS i (fiberMag):
- Median: -0.03
- Median Absolute Deviation: 0.21
- 1% percentile: -0.7520022773742676
- 99% percentile: 1.7319891357421873
DECam i (total) - SDSS i (petroMag):
- Median: -0.03
- Median Absolute Deviation: 0.21
- 1% percentile: -0.7520022773742676
- 99% percentile: 1.7319891357421873
KIDS i (aperture) - SDSS i (fiberMag):
- Median: -0.22
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8867145347595214
- 99% percentile: 0.4907330894470201
KIDS i (total) - SDSS i (petroMag):
- Median: -0.22
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8867145347595214
- 99% percentile: 0.4907330894470201
SUPRIME i (aperture) - SDSS i (fiberMag):
- Median: -0.29
- Median Absolute Deviation: 0.12
- 1% percentile: -0.9200366592407226
- 99% percentile: 0.5815175437927244
SUPRIME i (total) - SDSS i (petroMag):
- Median: -0.29
- Median Absolute Deviation: 0.12
- 1% percentile: -0.9200366592407226
- 99% percentile: 0.5815175437927244
GPC1 i (aperture) - SDSS i (fiberMag):
- Median: -0.42
- Median Absolute Deviation: 0.14
- 1% percentile: -1.0596820831298828
- 99% percentile: 0.5549477386474635
GPC1 i (total) - SDSS i (petroMag):
- Median: -0.42
- Median Absolute Deviation: 0.14
- 1% percentile: -1.0596820831298828
- 99% percentile: 0.5549477386474635
No sources have both SDSS z (fiberMag) and DECam z (aperture) values.
No sources have both SDSS z (petroMag) and DECam z (total) values.
SUPRIME z (aperture) - SDSS z (fiberMag):
- Median: -0.17
- Median Absolute Deviation: 0.19
- 1% percentile: -1.1571583557128906
- 99% percentile: 1.0003077316284184
SUPRIME z (total) - SDSS z (petroMag):
- Median: -0.17
- Median Absolute Deviation: 0.19
- 1% percentile: -1.1571583557128906
- 99% percentile: 1.0003077316284184
GPC1 z (aperture) - SDSS z (fiberMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.2993665313720701
- 99% percentile: 1.0381328201293956
GPC1 z (total) - SDSS z (petroMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.2993665313720701
- 99% percentile: 1.0381328201293956

III.b - Comparing J and K bands to 2MASS

The catalogue is cross-matched to 2MASS-PSC withing 0.2 arcsecond. We compare the UKIDSS total J and K magnitudes to those from 2MASS.

The 2MASS magnitudes are “Vega-like” and we have to convert them to AB magnitudes using the zero points provided on this page:

Band Fν - 0 mag (Jy)
J 1594
H 1024
Ks 666.7

In addition, UKIDSS uses a K band whereas 2MASS uses a Ks (“short”) band, this page give a correction to convert the K band in a Ks band with the formula:

$$K_{s(2MASS)} = K_{UKIRT} + 0.003 + 0.004 * (J−K)_{UKIRT}$$
In [14]:
# The AB zero point is 3631 Jy
j_2mass_to_ab = 2.5 * np.log10(3631/1595)
k_2mass_to_ab = 2.5 * np.log10(3631/666.7)
In [15]:
twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_GAMA-12.fits")
twomass_coords = SkyCoord(twomass['raj2000'], twomass['dej2000'])

idx, d2d, _ = twomass_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

twomass = twomass[mask]
ml_twomass_idx = idx[mask]
In [16]:
nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_wfcam_j'][ml_twomass_idx],
                labels=("2MASS J", "UKIDSS J (total)"))
UKIDSS J (total) - 2MASS J:
- Median: 0.06
- Median Absolute Deviation: 0.06
- 1% percentile: -0.8233773828031368
- 99% percentile: 0.7282878625581957
In [17]:
ukidss_ks_like = master_catalogue['m_wfcam_k'] + 0.003 + 0.004 * (
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "UKIDSS Ks-like (total)"))
UKIDSS Ks-like (total) - 2MASS Ks:
- Median: 0.11
- Median Absolute Deviation: 0.10
- 1% percentile: -0.838410065751889
- 99% percentile: 1.102716745084901
In [18]:
nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_vircam_j'][ml_twomass_idx],
                labels=("2MASS J", "VISTA J (total)"))
VISTA J (total) - 2MASS J:
- Median: 0.02
- Median Absolute Deviation: 0.08
- 1% percentile: -1.1603096778013053
- 99% percentile: 1.0473402899263549
In [19]:
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, master_catalogue['m_vircam_k'][ml_twomass_idx],
                labels=("2MASS Ks", "VISTA Ks (total)"))
VISTA Ks (total) - 2MASS Ks:
- Median: 0.05
- Median Absolute Deviation: 0.14
- 1% percentile: -1.0731929440172698
- 99% percentile: 1.2191779905072637

IV - Comparing aperture magnitudes to total ones.

In [20]:
nb_ccplots(
    master_catalogue['m_suprime_r'],
    master_catalogue['m_ap_suprime_r'] - master_catalogue['m_suprime_r'],
    "r total magnitude (HSC)", "r aperture mag - total mag (HSC)",
    master_catalogue["stellarity"],
    invert_x=True
)
Number of source used: 4366603 / 12369251 (35.30%)

V - Color-color and magnitude-color plots

In [21]:
nb_ccplots(
    master_catalogue['m_suprime_r'] - master_catalogue['m_suprime_i'],
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'],
    "r - i (HSC)", "J - K (UKIDSS)",
    master_catalogue["stellarity"]
)
Number of source used: 37754 / 12369251 (0.31%)
In [22]:
nb_ccplots(
    master_catalogue['m_suprime_i'] - master_catalogue['m_wfcam_j'],
    master_catalogue['m_suprime_r'] - master_catalogue['m_wfcam_k'],
    "WFC i - IRAC1", "r - i (WFC)",
    master_catalogue["stellarity"]
)
Number of source used: 37754 / 12369251 (0.31%)
In [23]:
nb_ccplots(
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'],
    master_catalogue['m_suprime_r'] - master_catalogue['m_wfcam_j'],
    "J - K (UKIDSS)", "r - J (HSC, UKIDSS)",
    master_catalogue["stellarity"]
)
Number of source used: 37757 / 12369251 (0.31%)
In [24]:
nb_ccplots(
    master_catalogue['m_decam_i'] - master_catalogue['m_decam_z'],
    master_catalogue['m_decam_z'] - master_catalogue['m_wfcam_j'],
    "i - z (DECaLS)", "z - J (DECaLS, UKIDSS)",
    master_catalogue["stellarity"]
)
Number of source used: 0 / 12369251 (0.00%)
HELP warning: no sources with observations in both bands